Scrap determination system and scrap determination method

ABSTRACT

Provided are a scrap determination system and method that can improve scrap determination techniques. A scrap determination system (1) comprises: a first scrap determination model (221) generated using teaching data including first learning images, and determines, based on a camera image, grade of scrap in the image and a ratio of the grade; a second scrap determination model (222) generated using teaching data including second learning images, and determines, based on the image, grade of scrap in the image and a ratio of the grade; a selection model (223) configured to determine which of the first scrap determination model (221) and the second scrap determination model (222) is to be used, based on the image; and an output section (24) configured to output information of each grade of scrap and a ratio of the grade determined based on the image using the model selected by the selection model (223).

TECHNICAL FIELD

The present disclosure relates to a scrap determination system and ascrap determination method.

BACKGROUND

In recent years, there has been demand to reuse waste such as scrap asrecyclable resources for effective resource utilization. For reuse ofwaste, it is necessary to determine recyclable resources. Wastedetermination methods not relying on human power have beenconventionally proposed (for example, JP 2017-109197 A (PTL 1)).

CITATION LIST Patent Literature

-   PTL 1: JP 2017-109197 A

SUMMARY Technical Problem

The technique described in PTL 1 is intended for determination of wastesuch as dismantled houses and disaster debris, and methods ofefficiently determining scrap such as metal are not studied. Forexample, iron scrap is distributed in the market as a reusable resourcerelated to iron, and is recycled into iron using electric heatingfurnaces or the like. Conventionally, grades of scrap are determinedvisually by workers at iron scrap processing sites. This is becausescrap metal pieces after crushing have various scales and the shape ofeach scrap is different and therefore the whole needs to be visuallyinspected in order to make grade determination. It is thus difficult toautomate scrap determination. Meanwhile, in visual determination worksby workers, determination results vary depending on the skill of theworkers. There is also a problem of aging of workers and difficulty insecuring personnel. Hence, scrap determination techniques have room forimprovement.

It could therefore be helpful to provide a scrap determination systemand a scrap determination method that can improve scrap determinationtechniques.

Solution to Problem

A scrap determination system according to one of the disclosedembodiments comprises: an acquisition section configured to acquire acamera image including scrap; a first scrap determination modelgenerated using teaching data including first learning images, andconfigured to determine, based on the camera image, each grade of thescrap included in the camera image and a ratio of the grade; a secondscrap determination model generated using teaching data including secondlearning images different from the first learning images, and configuredto determine, based on the camera image, each grade of the scrapincluded in the camera image and a ratio of the grade; a selection modelconfigured to determine which of the first scrap determination model andthe second scrap determination model is to be used, based on the cameraimage; and an output section configured to output information of eachgrade of the scrap and a ratio of the grade determined based on thecamera image using a model selected by the selection model out of thefirst scrap determination model and the second scrap determinationmodel.

A scrap determination method according to one of the disclosedembodiments is a scrap determination method that uses: a first scrapdetermination model generated using teaching data including firstlearning images, and configured to determine, based on a camera imageincluding scrap, each grade of the scrap included in the camera imageand a ratio of the grade; and a second scrap determination modelgenerated using teaching data including second learning images differentfrom the first learning images, and configured to determine, based onthe camera image, each grade of the scrap included in the camera imageand a ratio of the grade, the scrap determination method comprising:acquiring the camera image; selecting, based on the camera image, whichof the first scrap determination model and the second scrapdetermination model is to be used, by a selection model; and outputtinginformation of each grade of the scrap and a ratio of the gradedetermined based on the camera image using a model selected by theselection model out of the first scrap determination model and thesecond scrap determination model.

Advantageous Effect

It is thus possible to improve scrap determination techniques.

BRIEF DESCRIPTION OF THE DRAWINGS

In the accompanying drawings:

FIG. 1 is a schematic diagram illustrating the structure of a scrapdetermination system according to one of the disclosed embodiments;

FIG. 2 illustrates specific examples of camera images of scrap to bedetermined;

FIG. 3 is a schematic diagram illustrating a learning process by a firstscrap determination model;

FIG. 4 is a schematic diagram illustrating a determination process bythe first scrap determination model;

FIG. 5 is a schematic diagram illustrating a learning process by asecond scrap determination model;

FIG. 6 is a flowchart illustrating a scrap determination methodaccording to one of the disclosed embodiments; and

FIG. 7 is a conceptual diagram of normalization for each group dependingon the camera image zoom level.

DETAILED DESCRIPTION

Some of the disclosed embodiments will be described below, withreference to the drawings.

In the drawings, the same or corresponding parts are given the samereference signs. In the following description of embodiments, thedescription of the same or corresponding parts is omitted or simplifiedas appropriate.

FIG. 1 is a schematic diagram illustrating the overall structure of ascrap determination system 1 according to one of the disclosedembodiments. This embodiment describes the case where scrap to bedetermined is iron scrap, but scrap to be determined is not limited toiron scrap and may be, for example, other metal scrap.

Iron scrap can be roughly divided into two types based on its source.One is process scrap (also called mill scrap) generated in theproduction stage in the manufacturing industry. Process scrap isrecovered by recovery companies, and then distributed under differentnames such as new scrap (shindachi), steel turnings, and pig scrap. Mostof process scrap are taken by steelmakers without undergoing processing(intermediate processing). Process scrap is iron scrap of clear history,and is considered to be useful as with return scrap in terms of quality.Moreover, there is little possibility of foreign matter being mixed induring the generation, recovery, and transportation stages.

The other scrap is obsolete scrap generated as a result of aging ofsteel structures. Obsolete scrap includes scrap generated during therepair or damage stage. Obsolete scrap is generated in various scenessuch as building dismantling, machinery renewal, used automobiles,containers, etc., and in various shapes. Therefore, recovered obsoletescrap is subjected to processing such as sizing, crushing, and volumereduction in order to increase the feed efficiency in steelmaking, andthen treated as heavy scrap. Steel plate products such as homeappliances, automobile bodies, and vending machines are reduced involume mainly by crushing, and then subjected to magnetic separation toselect only iron. Since such obsolete scrap becomes diverse in each ofthe generation, recovery, and processing stages, grade determination isperformed after the processing. Grade determination for obsolete scrapis made based on the shape, i.e. the thickness, width, length, etc., ofthe scrap. Currently, in Japan, the Uniform Standards of Ferrous Scrapsestablished by the Japan Ferrous Raw Materials Association in 1996 iswidely used.

As mentioned earlier, grades of scrap are conventionally determinedvisually by workers at iron scrap processing sites. Such visualdetermination works by workers have problems such as variation indetermination results due to different skill levels of the workers. Inview of this, the scrap determination system 1 according to thisembodiment performs scrap determination based on camera images of ironscrap instead of visual determination by workers.

This embodiment describes an example of determining six types of grades,namely, HS, H1, H2, and H3 which are typical among scraps and L1 and L2of low iron quality such as rusted galvanized iron plates, although thegrades to be determined are not limited to such. The grades to bedetermined may include new scrap (shear chips), turnings (cuttingchips), and the like. Thus, the scrap grades to be determined in thisembodiment may include any shape of scrap grades according to the needsof manufacturing sites.

As illustrated in FIG. 1 , the scrap determination system 1 according tothis embodiment includes a plurality of cameras 10 and an informationprocessing device 20. The plurality of cameras 10 and the informationprocessing device 20 are connected via a network 30. The plurality ofcameras 10 are, for example, network cameras, and transmit capturedcamera images to the information processing device 20 via the network30. Although the number of cameras 10 is four in FIG. 1 , the number ofcameras 10 is not limited to such. The number of cameras 10 may be lessthan four, and may be one. The number of cameras 10 may be more thanfour. A camera image is an image of iron scrap taken when the iron scraphas been moved to a yard after being transported by a truck. FIG. 2illustrates specific examples of camera images taken by the cameras 10.As illustrated in FIG. 2 , scrap included in each camera image is amixture of iron scrap of a plurality of grades. The informationprocessing device 20 determines the grades of iron scrap in the cameraimage and the ratio of each grade, based on a plurality of modelsgenerated by machine learning. In actual operation, scrap is tradedbased on the weight ratio of each grade and the total weight of thescrap. This embodiment describes an example in which the informationprocessing device 20 determines the ratio relating to the weight ofscrap of each grade in a camera image.

The information processing device 20 includes a controller 21, a storage22, an acquisition section 23, and an output section 24.

The controller 21 includes at least one processor, at least onededicated circuit, or a combination thereof. Examples of the processorinclude a general-purpose processor such as a central processing unit(CPU), and a dedicated processor specialized in a specific process.Examples of the dedicated circuit include a field-programmable gatearray (FPGA) and an application specific integrated circuit (ASIC). Thecontroller 21 executes a process relating to the operation of theinformation processing device 20 while controlling each component in theinformation processing device 20.

The storage 22 includes at least one semiconductor memory, at least onemagnetic memory, at least one optical memory, or a combination of two ormore thereof. Examples of the semiconductor memory include a randomaccess memory (RAM) and a read only memory (ROM). The RAM is, forexample, a static random access memory (SRAM) or a dynamic random accessmemory (DRAM). The ROM is, for example, an electrically erasableprogrammable read only memory (EEPROM). For example, the storage 22functions as a main storage device, an auxiliary storage device, or acache memory. The storage 22 stores data used for the operation of theinformation processing device 20 and data obtained as a result of theoperation of the information processing device 20. For example, thestorage 22 stores a first scrap determination model 221, a second scrapdetermination model 222, and a selection model 223.

The first scrap determination model 221 is a learning model fordetermining each grade of scrap included in a camera image and the ratioof the grade based on the camera image. The first scrap determinationmodel 221 is generated based on teaching data including first learningimages. Each first learning image is an image of single-grade ironscrap. In detail, the first scrap determination model 221 is generatedby machine learning using a machine learning algorithm such as neuralnetwork, based on teaching data including first learning images andtrack record data of determination for the first learning images. FIG. 3schematically illustrates a learning process by the first scrapdetermination model 221. As illustrated in FIG. 3 , first learningimages which are each an image of single-grade iron scrap are input toan input layer of the first scrap determination model 221. Each image ofsingle-grade iron scrap is associated with track record data of a gradedetermined by an operator. The weight coefficients between neurons areadjusted using the teaching data, and the learning process by the firstscrap determination model 221 is performed. Although the respectiveimages are associated with HS, H1, H2, H3, L1, and L2 in FIG. 3 , thegrades are not limited to such. An iron scrap image of any other grademay be used as an image of single-grade iron scrap. Although the firstscrap determination model 221 is generated based on a multi-layerperceptron composed of an input layer, a hidden layer, and an outputlayer in FIG. 3 , the first scrap determination model 221 is not limitedto such. The first scrap determination model 221 may be generated by anyother machine learning algorithm. For example, the first scrapdetermination model 221 may be generated based on a machine learningalgorithm such as convolutional neural network (CNN) or deep learning.

When determining each grade of scrap included in a camera image and theratio of the grade, the controller 21 determines, based on the arearatio of scrap of each grade in the camera image, the ratio of thescrap, using the first scrap determination model 221. FIG. 4schematically illustrates a determination process by the first scrapdetermination model 221. As illustrated in FIG. 4 , the controller 21inputs an image of each part of a camera image divided in a grid patternto the first scrap determination model 221, and determines the grade ofscrap in the partial image. The controller 21 randomly extracts partialimages of the camera image divided in a grid pattern and determines thegrade of scrap in each partial image, and calculates the area ratio ofeach grade of scrap in the image. Further, the controller 21 convertsthe area ratio into the weight ratio based on the bulk density of eachscrap. Thus, the controller 21 calculates each grade of scrap includedin the camera image and the ratio of the grade. Although an example inwhich the controller 21 randomly extracts partial images of the cameraimage and calculates the area ratio is described here, the determinationprocess is not limited to such. For example, the controller 21 maycalculate the area ratio based on all partial images constituting thecamera image.

The second scrap determination model 222 is a learning model fordetermining each grade of scrap included in a camera image and the ratioof the grade based on the camera image. The second scrap determinationmodel 222 is generated based on teaching data including second learningimages different from the first learning images. Each second learningimage is an image of mixed-grade iron scrap. Mixed-grade iron scrap isiron scrap containing iron scraps of a plurality of grades. In detail,the second scrap determination model 222 is generated by machinelearning using a machine learning algorithm such as neural network,based on teaching data including second learning images and track recorddata of determination for the second learning images. FIG. 5schematically illustrates a learning process by the second scrapdetermination model 222. As illustrated in FIG. 5 , second learningimages which are each an image of mixed-grade iron scrap are input to aninput layer of the second scrap determination model 222. Each image ofmixed-grade iron scrap is associated with track record data of a gradeand the ratio of the grade determined by an operator. The weightcoefficients between neurons are adjusted using the teaching data, andthe learning process by the model is performed. Although the secondscrap determination model 222 is generated based on a multi-layerperceptron composed of an input layer, a hidden layer, and an outputlayer in FIG. 5 , the second scrap determination model 222 is notlimited to such. The second scrap determination model 222 may begenerated by any other machine learning algorithm. For example, thesecond scrap determination model 222 may be generated based on a machinelearning algorithm such as convolutional neural network (CNN) or deeplearning. When determining each grade of scrap included in a cameraimage and the ratio of the grade using the second scrap determinationmodel 222, the controller 21 randomly extracts partial images of thecamera image divided and determines the grade of scrap in each partialimage, and calculates the weight ratio of each grade of scrap in theimage, as in the first scrap determination model 221. This is becausethe determination accuracy can be improved by dividing an image anddetermining randomly selected images many times. Although an example inwhich the controller 21 randomly extracts partial images of the cameraimage and calculates the weight ratio of each grade of scrap isdescribed here, the determination process is not limited to such, as inthe first scrap determination model 221. For example, the controller 21may calculate the weight ratio based on all partial images constitutingthe camera image.

The selection model 223 is a model for estimating, based on a cameraimage, which of the first scrap determination model 221 and the secondscrap determination model 222 outputs a more probable solution whendetermining each grade of scrap included in the camera image and theratio of the grade. The selection model 223 selects the model thatoutputs a more probable solution, based on the estimation result. Thecontroller 21 determines each grade of scrap and the ratio of the gradebased on the camera image, using the model selected by the selectionmodel 223. In other words, the selection model 223 determines which ofthe first scrap determination model 221 and the second scrapdetermination model 222 is to be used for scrap grade determination,based on the camera image. Teaching data for the selection model 223includes a camera image of scrap acquired from each camera 10 via thenetwork 30, each grade of scrap and the ratio of the grade estimated bythe first scrap determination model 221, each grade of scrap and theratio of the grade estimated by the second scrap determination model222, and track record data of each grade and the ratio of the gradedetermined by an operator. The track record data for model selection isbased on the determination result when inputting the camera image toeach of the first scrap determination model 221 and the second scrapdetermination model 222 and the result of the operator determining thegrades and the ratio of each grade for the camera image. The selectionmodel 223 is an estimation model generated by machine learning using amachine learning algorithm such as neural network, based on suchteaching data. For example, the selection model 223 is generated basedon a machine learning algorithm such as multi-layer perceptron,convolutional neural network (CNN), or deep learning.

The acquisition section 23 acquires a camera image including scrap fromeach camera 10 via the network 30. The acquisition section 23 includesat least one communication interface. Examples of the communicationinterface include a LAN interface, a WAN interface, an interface thatcomplies with a mobile communication standard such as Long TermEvolution (LTE), 4G (4th generation), or 5G (5th generation), and aninterface that complies with short-range wireless communication such asBluetooth® (Bluetooth is a registered trademark in Japan, othercountries, or both). The acquisition section 23 receives data used forthe operation of the information processing device 20, and transmitsdata obtained as a result of the operation of the information processingdevice 20.

The output section 24 includes at least one output interface. An exampleof the output interface is a display. The display is, for example, aliquid crystal display (LCD) or an organic electroluminescent (EL)display. The output section 24 outputs data obtained as a result of theoperation of the information processing device 20. The output section 24may be connected to the information processing device 20 as an externaloutput device, instead of being included in the information processingdevice 20. As a connection method, any method such as USB, HDMI®, orBluetooth® may be used.

The functions of the information processing device 20 are implemented bya program according to this embodiment being executed by a processorcorresponding to the controller 21. That is, the functions of theinformation processing device 20 are implemented by software. Theprogram causes a computer to execute the operation of the informationprocessing device 20, thus causing the computer to function as theinformation processing device 20. In other words, the computer executesthe operation of the information processing device 20 according to theprogram to thus function as the information processing device 20.

In this embodiment, the program can be recorded in a computer-readablerecording medium. The computer-readable recording medium includes anon-transitory computer-readable medium, such as a magnetic recordingdevice, an optical disc, a magneto-optical recording medium, or asemiconductor memory. The program is distributed, for example, byselling, transferring, or renting a portable recording medium having theprogram recorded therein, such as a digital versatile disc (DVD) or acompact disc read only memory (CD-ROM). The program may be distributedby storing the program in a storage of a server and transmitting theprogram from the server to another computer. The program may be providedas a program product.

In this embodiment, for example, the computer once stores, in the mainstorage device, the program recorded in the portable recording medium orthe program transmitted from the server, and then reads the programstored in the main storage device by the processor and executes aprocess according to the read program by the processor. The computer mayread the program directly from the portable recording medium and executethe process according to the program. Each time the computer receivesthe program from the server, the computer may execute the processaccording to the received program. Without the program being transmittedfrom the server to the computer, the process may be executed byapplication service provider (ASP) services that implement the functionsby only execution instruction and result acquisition. The programincludes information that is to be processed by an electronic computerand is equivalent to a program. For example, data that is not a directcommand to the computer but has the property of defining a process ofthe computer is “equivalent to a program”.

All or part of the functions of the information processing device 20 maybe implemented by a dedicated circuit corresponding to the controller21. That is, all or part of the functions of the information processingdevice 20 may be implemented by hardware.

A scrap determination method executed by the scrap determination system1 according to one of the disclosed embodiments will be described below.FIG. 6 is a flowchart illustrating a scrap determination methodaccording to one of the disclosed embodiments.

First, each camera 10 in the scrap determination system 1 takes a cameraimage including scrap (step S10). The camera 10 then transmits thecamera image to the information processing device 20 via the network 30.The acquisition section 23 in the information processing device 20acquires the camera image via the network 30 (step S20).

Following this, the controller 21 determines, based on the acquiredcamera image, which of the first scrap determination model 221 and thesecond scrap determination model 222 is to be used, using the selectionmodel 223 (step S30).

The controller 21 then determines each grade of scrap included in thecamera image and the ratio of the grade, using the model selected by theselection model 223 out of the first scrap determination model 221 andthe second scrap determination model 222 (step S40).

The controller 21 then instructs the output section 24 to output thegrade of scrap and the ratio determined in step S40. The output section24 outputs the grade of scrap and the ratio determined in step S40 (stepS50).

Thus, the scrap determination system 1 according to one of the disclosedembodiments can automatically determine scrap grades and their ratiosfrom a camera image of scrap taken by each camera 10, using the firstscrap determination model 221 or the second scrap determination model222. Here, which of the first scrap determination model 221 and thesecond scrap determination model 222 is to be used is selected by theselection model 223, so that a more appropriate model is automaticallyselected. In other words, the scrap determination system 1 according toone of the disclosed embodiments can determine and output scrap gradesand their ratios without manual intervention. The scrap determinationsystem 1 according to one of the disclosed embodiments can thereforeimprove scrap determination techniques.

While the presently disclosed techniques have been described by way ofthe drawings and embodiments, various changes and modifications may beeasily made by those of ordinary skill in the art based on the presentdisclosure. Such changes and modifications are therefore included in thescope of the present disclosure. For example, the functions included inthe components, steps, etc. may be rearranged without logicalinconsistency, and a plurality of components, steps, etc. may becombined into one component, step, etc. and a component, step, etc. maybe divided into a plurality of components, steps, etc.

For example, in the learning process and the determination process ofeach of the first scrap determination model 221, the second scrapdetermination model 222, and the selection model 223, the controller 21may use zoom information corresponding to each image. In the case ofusing zoom information, each camera 10 transmits, together with a cameraimage, zoom information of open network video interface forum (ONVIF)data corresponding to the camera image, to the information processingdevice 20 via the network 30. For example, the first learning images,the second learning images, and the camera images may each be normalizedbased on zoom information corresponding to the image. In detail, thecontroller 21 normalizes each of the first learning images, the secondlearning images, and the camera images to a predetermined magnificationfactor based on the zoom information corresponding to the image. Thecontroller 21 then performs the learning process using the normalizedfirst learning images or second learning images, and performs thedetermination process based on the camera images. As a result ofnormalizing each image by such a normalization process, thedetermination accuracy of the scrap determination system 1 can beenhanced.

In the case of normalizing each image to a predetermined magnificationfactor based on the corresponding zoom information, the controller 21may classify images into groups and perform normalization to a differentmagnification factor for each group based on zoom information. FIG. 7 isa conceptual diagram of normalization for each group of camera images.In FIG. 7 , each camera image is classified depending on themagnification factor. Specifically, in the case where the magnificationfactor of the camera image is in a range R₀₁ of x₀ or more and less thanx₁ (hereafter also referred to as “first range R₀₁”), the camera imageis classified into a first group. In the case where the magnificationfactor of the camera image is in a range R₁₂ of x₁ or more and less thanx₂ (hereafter also referred to as “second range R₁₂”), the camera imageis classified into a second group. In the case where the magnificationfactor of the camera image is in a range R₂₃ of x₂ or more and less thanx₃ (hereafter also referred to as “third range R₂₃”), the camera imageis classified into a third group. In the case where the magnificationfactor of the camera image is in a range R₃₄ of x₃ or more and less thanx₄ (hereafter also referred to as “fourth range R₃₄”), the camera imageis classified into a fourth group. In the case where the magnificationfactor of the camera image is in a range R₄₅ of x₄ or more and less thanx₅ (hereafter also referred to as “fifth range R₄₅”), the camera imageis classified into a fifth group. The camera images in the first rangeR₀₁, the second range R₁₂, the third range R₂₃, the fourth range R₃₄,and the fifth range R₄₅ are respectively normalized to magnificationfactors X₀₁, X₁₂, X₂₃, X₃₄, and X₄₅ each as a normal magnificationfactor of the corresponding range. That is, the first learning images,the second learning images, and the camera images are each normalized toa magnification factor that differs depending on the zoom informationcorresponding to the image. In other words, the controller 21 normalizeseach camera image to one of a plurality of normal magnification factorsthat is selected based on the zoom information of the camera image. Inthis way, variation in image resolution caused by excessive enlargementor reduction of camera images can be suppressed, and the determinationaccuracy of the scrap determination system 1 can be enhanced. Althoughthe controller 21 classifies images into five groups and normalizes eachimage to the corresponding one of the five magnification factors in FIG.7 , the classification is not limited to such. For example, the numberof groups into which images are classified may be four or less or six ormore, and the controller 21 may normalize each image to a magnificationfactor that differs depending on the group.

Although an example in which zoom information of camera images is usedin each of the learning process and the determination process isdescribed above, the information used is not limited to such. Forexample, the scrap determination system 1 may use at least part of ONVIFdata obtained from each camera 10, in each of the learning process andthe determination process. The ONVIF data includes pan, tilt, and zoominformation. That is, the scrap determination system 1 may perform thelearning process and the determination process using at least one ofpan, tilt, and zoom information.

For example, in the learning process and the determination process ofeach of the first scrap determination model 221, the second scrapdetermination model 222, and the selection model 223, the controller 21may use information about a carry-in company that carries scrap in. Thismakes it possible to perform determination in consideration of thetendency of scrap carried in by each carry-in company, so that thedetermination accuracy of the scrap determination system 1 can beimproved.

For example, the scrap determination system 1 and scrap determinationsystem 2 may further accumulate each camera image used in thedetermination process as new teaching data after the determination. Thecontroller 21 may then relearn, based on the camera image, the firstscrap determination model 221, the second scrap determination model 222,and the selection model 223, using the result of the operatordetermining each grade and the ratio of the grade. For example, if thereis a problem with the output result (determination result), the outputinformation having the problem, the camera image and the track recorddata corresponding to the information may be used as teaching data toperform relearning of at least one of the first scrap determinationmodel 221, the second scrap determination model 222, and the selectionmodel 223. This can improve the determination accuracy and speed of thefirst scrap determination model 221, the second scrap determinationmodel 222, and the selection model 223.

Although the foregoing embodiment describes the case where a cameraimage is an image of iron scrap taken when the iron scrap has been movedto a yard after being transported by a truck, the camera image is notlimited to such. For example, the camera image may be an image of scrapin a state of being lifted with a crane at a manufacturing site. In thiscase, a lighting device for illuminating the scrap during photographingmay be used. A clear camera image can thus be obtained.

REFERENCE SIGNS LIST

-   -   1 scrap determination system    -   10 camera    -   20 information processing device    -   21 controller    -   22 storage    -   23 acquisition section    -   24 output section    -   221 first scrap determination model    -   222 second scrap determination model    -   223 selection model    -   30 network

1. A scrap determination system comprising: an acquisition sectionconfigured to acquire a camera image including scrap; a first scrapdetermination model generated using teaching data including firstlearning images, and configured to determine, based on the camera image,each grade of the scrap included in the camera image and a ratio of thegrade; a second scrap determination model generated using teaching dataincluding second learning images different from the first learningimages, and configured to determine, based on the camera image, eachgrade of the scrap included in the camera image and a ratio of thegrade; a selection model configured to determine which of the firstscrap determination model and the second scrap determination model is tobe used, based on the camera image; and an output section configured tooutput information of each grade of the scrap and a ratio of the gradedetermined based on the camera image using a model selected by theselection model out of the first scrap determination model and thesecond scrap determination model.
 2. The scrap determination systemaccording to claim 1, wherein the first learning images are each animage of single-grade iron scrap, and when determining each grade of thescrap included in the camera image and a ratio of the grade using thefirst scrap determination model, the ratio of the grade is determinedbased on an area ratio of scrap of the grade in the camera image.
 3. Thescrap determination system according to claim 1, wherein the secondlearning images are each an image of mixed-grade iron scrap.
 4. Thescrap determination system according to claim 1, wherein the firstlearning images, the second learning images, and the camera image areeach normalized based on zoom information corresponding to the image. 5.The scrap determination system according to claim 4, wherein the firstlearning images, the second learning images, and the camera image areeach normalized to a magnification factor that differs depending on thezoom information corresponding to the image.
 6. The scrap determinationsystem according to claim 1, wherein at least one of the first scrapdetermination model, the second scrap determination model, and theselection model is relearned based on the camera image and theinformation output by the output section.
 7. A scrap determinationmethod that uses: a first scrap determination model generated usingteaching data including first learning images, and configured todetermine, based on a camera image including scrap, each grade of thescrap included in the camera image and a ratio of the grade; and asecond scrap determination model generated using teaching data includingsecond learning images different from the first learning images, andconfigured to determine, based on the camera image, each grade of thescrap included in the camera image and a ratio of the grade, the scrapdetermination method comprising: acquiring the camera image; selecting,based on the camera image, which of the first scrap determination modeland the second scrap determination model is to be used, by a selectionmodel; and outputting information of each grade of the scrap and a ratioof the grade determined based on the camera image using a model selectedby the selection model out of the first scrap determination model andthe second scrap determination model.
 8. The scrap determination systemaccording to claim 2, wherein the second learning images are each animage of mixed-grade iron scrap.
 9. The scrap determination systemaccording to claim 2, wherein the first learning images, the secondlearning images, and the camera image are each normalized based on zoominformation corresponding to the image.
 10. The scrap determinationsystem according to claim 9, wherein the first learning images, thesecond learning images, and the camera image are each normalized to amagnification factor that differs depending on the zoom informationcorresponding to the image.
 11. The scrap determination system accordingto claim 2, wherein at least one of the first scrap determination model,the second scrap determination model, and the selection model isrelearned based on the camera image and the information output by theoutput section.